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The MedNovel Model AiVUS 1000 is indicated for use as an adjunct to mammography for B-mode ultrasonic imaging of patient's breast when used with an automatic scanning linear array probe. The device is not intended to be used as a replacement for mammography.

It is an adjunct (add on) accessory for existing ultrasound imaging systems and is intended to control position and movement of ultrasound transducers for the systematic acquisition of 2 dimensional image slices throughout a volume of interest.

The MedNovel Model AiVUS 1000 system is capable of visually reconstructing the ultrasonic images for review with DICOM type files which will assist the doctor in making diagnoses of any abnormalities detected.

The Model AiVUS 1000 is a transducer guiding adjunct which is intended to work in combination with specified legally marketed Ultrasound Diagnostic System (MyLab Gamma, K161359) and Transducer (SL1543, K132231) and previously released exam tables.

The Model AiVUS 1000 system is comprised of a patient scanning system and display system.

The Model AiVUS 1000 performs an automatic scan of the breast(s) using a probe guiding mechanism(s) imbedded within the scan module. The imbedded system software plots the path of the automated scan, records the ultrasonic images from paired ultrasound system, and has the capability to both display the real-time B-mode ultrasonic images acquired and after the fact review of the breast exam in DICOM supported file format as a means of providing adjunctive information after a patient obtains a mammogram. This adjunctive information may be useful to physicians wishing to have additional information to make diagnoses of any abnormalities detected.

The patient scanning system consists of a scan module and associated assembly and electronics. The scan module is centered above a patient's breast and then performs scanning by using the ultrasound linear transducer arrays. While scanning, the real-time images are acquired by video frame grabber. Acquired images are then reconstructed by the Model AIVUS 1000 system software to display the breast images on the Model AIVUS 1000 display system.

The Model AiVUS 1000 display system has two adjustable touch panels and associated assembly and electronics.

The MedNovel Model AiVUS 1000 is an Advanced Intelligent Volumetric Ultrasound System. The purpose of the study was to demonstrate substantial equivalence to predicate devices (K081148 – Acuson S2000 ABVS Ultrasound System, K141870 – Breast Volume Navigator and K963807 – TomTec Echo-Scan).

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance:

   The provided document does not explicitly present a table of specific numerical acceptance criteria for image quality or diagnostic performance, nor does it present reported device performance in a quantified manner against such criteria. Instead, the summary states:

   Acceptance Criteria (Implied)	Reported Device Performance
   Imaging Quality	"generate diagnostic quality images that are at least as good as those of the current standard of care, handheld ultrasound examination."
   Compliance with Standards	"Complies with the aforementioned international and FDA-recognized consensus standards and FDA guidance documents."
   Intended Use Adequacy	"Meet the accept criteria and is adequate for its intended use."

2. Sample Size Used for the Test Set and Data Provenance:

   • Test Set Sample Size: The document mentions "clinical sample images in appendix H" and refers to "different size of breast types (A, B, C, D cup size) and different subject ethnicities (Han, Caucasoid, African) with a variety of lesions were captured during the collection of the clinical sample image data". However, a specific numerical sample size for the test set is not provided.
   • Data Provenance: The document implies the data is retrospective as it refers to "clinical sample images in appendix H were provided to support the ability of proposed Model AiVUS 1000 to generate diagnostic quality images". The country of origin for the data is not specified.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

   This information is not provided in the document.

4. Adjudication Method for the Test Set:

   This information is not provided in the document.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   No MRMC comparative effectiveness study was mentioned. The device is intended to assist physicians by providing adjunctive information, not to replace mammography or human interpretation entirely. "The MedNovel Model AiVUS 1000 system is capable of visually reconstructing the ultrasonic images for review with DICOM type files which will assist the doctor in making diagnoses of any abnormalities detected." Therefore, an effect size of human readers improving with AI vs. without AI assistance is not applicable/provided.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

   The document states: "The AiVUS 1000 system is intended for acquiring ultrasound images only. Tools for evaluating abnormalities identified (size, location, etc.) are contained in the workstation software which is a standalone product and will be submitted for clearance later." This suggests that the current device only acquires images and does not perform automated diagnostic interpretation. Therefore, a standalone performance study of an algorithm is not applicable for the AiVUS 1000 itself, as its primary function is image acquisition and reconstruction, not autonomous diagnostic analysis. The statement "Verification testing was performed in Image Simulation Tests on breast phantoms" indicates some form of objective testing, but not for autonomous diagnostic performance.

7. Type of Ground Truth Used:

   The document states that "clinical sample images in appendix H were provided to support the ability of proposed Model AiVUS 1000 to generate diagnostic quality images that are at least as good as those of the current standard of care, handheld ultrasound examination." While clinical images were used, the specific nature of the "ground truth" (e.g., expert consensus, pathology, outcome data) that confirmed the "diagnostic quality" is not explicitly detailed. It implies a comparison against the "current standard of care," which would involve expert interpretation, but the formal process for establishing this ground truth is not outlined.

8. Sample Size for the Training Set:

   This information is not provided in the document. The document refers to "verification/validation tests results metrics DOC0000101 Rev A" and "Image Simulation Tests on breast phantoms," but does not detail a training set sample size for any machine learning component of the image acquisition. Given the device's stated function is image acquisition and reconstruction, not AI-driven diagnosis, a "training set" in the context of deep learning might not be applicable in the traditional sense for this specific device.

9. How the Ground Truth for the Training Set Was Established:

   As a training set is not explicitly mentioned and the device focuses on image acquisition and reconstruction rather than AI-driven diagnostic interpretation, the method for establishing ground truth for a training set is not applicable/provided.

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